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Hybrid prognostics for predictive maintenance:Combining physics-based and data-driven methods to overcome prognostic challenges
Get PDFPredictive maintenance is a growing research field, aiming to perform maintenance only when it is required. Prognostic algorithms are essential to achieve this, as predictions of upcoming failures help to increase availability of systems, utilize the full life time of equipment and improve maintenance logistics. Since sensors are getting cheaper and data storage and processing have become cheaper and more efficient during the fourth industrial revolution, there is a lot of interest in data-driven prognostic algorithms. However, high data requirements limit applicability is many practical applications. Physics-based prognostic models yield quantitative relations between system usage and degradation independent from historical data, but the development of such physics-of-failure models is complex and expensive. Because both data-driven and physics-based models have their advantages and limitations, combinations of both types of methods have the potential to get rid of the limitations and profit from the benefits.When both loads and the condition of a component can be monitored, physics-of-failure models can be updated in real-time using Bayesian filtering algorithms. This results in updated quantitative relations between loads and degradation, calibrated for a specific component. The first part of this thesis describes how such methods can be applied for components in variable operating conditions and implements it in a generic prognostic framework.These Bayesian filters preferably receive a direct measure of degradation, such as crack length or the amount of removed material. In many practical applications it is only possible to measure indirect consequences of degradation, such as increased vibration levels, elevated temperatures or acoustic emissions. Therefore, the second part of this thesis focuses on improving quantitative diagnostics to act as input for prognostic algorithms.Because of their modularity and applicability in multiple physical domains, bond graphs are proposed to simulate faults to enhance quantitative diagnostics. A combined diagnostic and prognostics framework is developed which is suitable for prognostics under varying operating conditions, when only limited historical run-to-failure data are available. One of the biggest challenges remains validation of the methods on a real-world case study, as the lack of real-world data is one of the biggest challenges from which this research partly originated.<br/
Global survey of land surveying and geomatics education
No full textWith rapid advancements in geospatial technology and its applications, understanding the status of academic education in land surveying and geomatics has become essential. A recent global survey by FIG Commission 2 has provided insight into the curricula, goals, methodology and relevance of the various disciplines taught academically within this field. By contributing to building a robust global community for academic exchange and professional collaboration, this ongoing research can also help to address common challenges related to research, curriculum development and professional standards
Hydrogen radical induced Zn transfer to Ru film surfaces and influence of ambient atmosphere exposure on Zn adsorbates on Ru
Get PDFIn this article, for the first time, we report the transfer of Zn from contaminated walls in an ultra-high vacuum chamber to Ru film surfaces, induced by hydrogen radicals. In the presence of atomic H, Zn forms volatile Zn hydrides. We propose that Ru acts as a catalytically active surface for decomposing Zn hydrides, which leads to adsorption of Zn on Ru. The this way deposited Zn layer is nearly atomically flat with no specific surface morphology. The kinetics of adsorption and etching varies with the amount of adsorbed Zn. We hypothesize that the change in the Ru electronic structure, the charge transfer from Ru to Zn, and the availability of Ru active sites play a role. 0.7 Zn monolayer (ML) formed ∼ 1 ZnO ML after ambient atmosphere exposure. This ZnO layer prevents Ru oxidation and samples showed no morphology change of the Zn deposit upon storage in ambient atmosphere for 6 weeks. Ru surfaces with ≤ 0.8 ZnO ML showed conversion of a 2D layer of Zn adsorbates to 3D agglomerates, accompanied with native oxide formation on Ru. Observations may find use in the development of contamination mitigation strategies, metrology, and fabrication of 2D materials.</p
Pixels and people:Exploring the dynamics of engagement and disengagement in Minecraft's multiplayer realm
Get PDFEngagement is often viewed as the holy grail of digital experiences, particularly video games, while disengagement is seen as a consequence of poor design. This study attempts to flip the script, exploring disengagement as a natural part of the user journey rather than a failure of game design. Focusing on the popular open-world game Minecraft, the research investigates dynamics triggering engagement and disengagement in the multiplayer mode through the lenses of established frameworks like the Process Model of Engagement as well as the Mechanics, Dynamics, and Aesthetics model. Semi-structured focus-group interviews conducted with 15 participants analyzed using Mayring's content analysis revealed social connections, novelty, progression, goal-driven gameplay, and adrenaline-fueled combat as key drivers that keep players hooked. Yet, the very act of achieving goals, absence of friends, overplay, setbacks, skill gaps, negative interactions, and the demands of the real world can trigger a powerful urge to disengage. Far from a design flaw, this research expands the body of literature suggesting that disengagement is a vital component of user autonomy. By redefining success beyond mere engagement metrics, the study paves the way for responsible gaming practices that empower players to make informed choices about their level of involvement. It beckons us to embrace a holistic vision of genuinely sustainable, ethical, and meaningful digital experiences that respect user autonomy and cultivate healthy engagement patterns.</p
Perception of the 2021 floods and their mental health, and social well-being among older adults in the Ahr Valley, Germany
Get PDFProtecting human health is a fundamental priority in contemporary society. According to the World Health Organization (WHO) Constitution, "Health is a state of complete physical, mental, and social well-being, and not merely the absence of disease or infirmity." While the physical health of older adults often receives considerable attention after flooding events, their mental and social well-being remains underexplored. The 2021 floods in the Ahr Valley, Germany, had a devastating impact on local communities, particularly on older adults who are more vulnerable to the aftermath of natural disasters. This study explores the perceptions of floods among individuals aged 65 and older, focusing on their mental health and social well-being. Using a mixed-methods approach, we conducted surveys and in-depth interviews to collect first-hand data on their experiences and coping mechanisms. Our findings highlight the multifaceted challenges faced by this population, including heightened psychological distress, disruption of social networks, and concerns over long-term recovery.This research underscores the need for targeted interventions to address the mental and social health needs of older adults in disaster-affected areas. By enhancing scientific understanding of the complex interplay between natural disasters and public health, the study aims to inform policymakers, healthcare providers, and social workers, ultimately improving the quality and effectiveness of post-disaster health services for older adults
Embedded test instruments for ageing-aware multi-processor system-on-chips
Get PDFAs Complementary Metal-Oxide-Semiconductor (CMOS) technology advances, integrated circuits (or chips) are becoming increasingly complex and powerful. These chips are widely employed in automotive, aerospace, industrial control, and medical equipment. Reliability is paramount in such safety-critical applications, as even minor failures can lead to serious consequences. However, the increased complexity of modern chips also accelerates ageing, resulting in performance degradation over time.This thesis focuses on enhancing the long-term reliability of these systems by integrating specialized on-chip sensors known as Embedded Test Instruments (ETIs). These ETIs continuously monitor the chip’s behaviour, assessing critical parameters such as operating speed, power dissipation, and temperature. Monitoring these factors enables early detection of wear and degradation, allowing for timely interventionbefore failures occur. A central contribution of this work is the development of a methodology to ensure that ETIs are both reliable and highly correlatable. The proposed ETIs can accurately measure slack time in critical paths, detect variations in supply current, monitor voltage droop due to switching activity, and sense on-chip temperature fluctuations. The designs were implemented and evaluated using 40nm CMOS technology, demonstrating high accuracy and efficiency.Moreover, this research illustrates the application of these correlatable ETIs in two key areas: end-of-life (EOL) prediction and hardware security. For lifetime prediction, a data fusion methodology based on principal component analysis (PCA) aggregates multiple ETI outputs to improve the forecasting of degradation in critical circuit components. For hardware security, a system-level methodology shows howvoltage-droop and temperature ETIs can enhance the stability of Physical Unclonable Functions (PUFs).Overall, this work contributes to the development of more dependable electronic systems through on-chip monitoring, promoting enhanced reliability and security
Ready, set, know: The relationship between actual and perceived knowledge of cybercrime and the intentions to engage in self-protective behaviour
No full textThis study examined the role of actual and perceived knowledge on individuals’ intentions to engage in self-protective behaviour. Participants (N = 222) completed an online questionnaire including an actual knowledge test of cybercrime, protection measures, and items measuring constructs of Protection Motivation Theory. Regression analyses showed that actual knowledge is a predictor for intention to engage in self-protective behaviour and the predictive power was stronger than for perceived knowledge. Overall, we find that both perceived and actual knowledge are crucial constructs for self-protective behaviour, however, they activate different types of efficacy. Perceived knowledge is a stronger predictor for self-efficacy, whereas actual knowledge is a better predictor for response efficacy. Implications for future interventions and research are discussed, in which emphasis should be placed on providing knowledge, increasing user confidence, and the effects of these interventions.</p
Ankle Sensor-Based Detection of Freezing of Gait in Parkinson’s Disease in Semi-Free Living Environments
Get PDFFreezing of gait (FOG) is a motor symptom experienced by people with Parkinson’s Disease (PD) where they feel like they are glued to the floor. Accurate and continuous detection is needed for effective cueing to prevent or shorten FOG episodes. A convolutional neural network (CNN) was developed to detect FOG episodes in data recorded from an inertial measurement unit (IMU) on a PD patient’s ankle under semi-free living conditions. Data were split into two sets: one with all movements and another with walking and turning activities relevant to FOG detection. The CNN model was evaluated using five-fold cross-validation (5Fold-CV), leave-one-subject-out cross-validation (LOSO-CV), and performance metrics such as accuracy, sensitivity, precision, F1-score, and AUROC; Data from 24 PD participants were collected, excluding three with no FOG episodes. For walking and turning activities, the CNN model achieved AUROC = 0.9596 for 5Fold-CV and AUROC = 0.9275 for LOSO-CV. When all activities were included, AUROC dropped to 0.8888 for 5Fold-CV and 0.9017 for LOSO-CV; the model effectively detected FOG in relevant movement scenarios but struggled with distinguishing FOG from other inactive states like sitting and standing in semi-free-living environments.</p
Development of simulated human milk ultrafiltrate (SHMUF) for analysis of native particles in human milk
Get PDFResearch into human milk often involves freezing, thawing and diluting. However, these steps influence the integrity of native milk particles, such as casein micelles (CMs) and extracellular vesicles (EVs). Correct sample preparation is vital for research into these particles, but validated protocols are scarce in the literature. Here, we design, synthesise and evaluate a novel medium for dilution: simulated human milk ultrafiltrate (SHMUF), aimed to preserve particle integrity. We evaluate the stability of fresh and frozen/thawed human milk in SHMUF, bovine simulated milk ultrafiltrate (SMUF), phosphate buffered saline (PBS) and demineralised water through changes in light scattering in optical transmission. Light scattering by human milk diluted with SHMUF remains stable for 10 h, whereas substantial changes are observed for milk samples diluted with the other media. Likewise, freezing and thawing cause changes in light scattering. We conclude that SHMUF most optimally preserves native particles, and that – ideally – freezing and thawing should be avoided.</p